Abstract: The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Abstract: PURPOSE: The antifungal drugs ketoconazole and itraconazole reduce serum concentrations of 4beta-hydroxycholesterol, which is a validated marker for hepatic cytochrome P450 (CYP) 3A4 activity. We tested the effect of another antifungal triazole agent, fluconazole, on serum concentrations of different sterols and oxysterols within the cholesterol metabolism to see if this inhibitory reaction is a general side effect of azole antifungal agents. METHODS: In a prospective, double-blind, placebo-controlled, two-way crossover design, we studied 17 healthy subjects (nine men, eight women) who received 400 mg fluconazole or placebo daily for 8 days. On day 1 before treatment and on day 8 after the last dose, fasting blood samples were collected. Serum cholesterol precursors and oxysterols were measured by gas chromatography-mass spectrometry-selected ion monitoring and expressed as the ratio to cholesterol (R_sterol). RESULTS: Under fluconazole treatment, serum R_lanosterol and R_24,25-dihydrolanosterol increased significantly without affecting serum cholesterol or metabolic downstream markers of hepatic cholesterol synthesis. Serum R_4beta-, R_24S-, and R_27-hydroxycholesterol increased significantly. CONCLUSION: Fluconazole inhibits the 14alpha-demethylation of lanosterol and 24,25-dihydrolanosterol, regulated by CYP51A1, without reduction of total cholesterol synthesis. The increased serum level of R_4beta-hydroxycholesterol under fluconazole treatment is in contrast to the reductions observed under ketoconazole and itraconazole treatments. The question, whether this increase is caused by induction of CYP3A4 or by inhibition of the catabolism of 4beta-hydroxycholesterol, must be answered by mechanistic in vitro and in vivo studies comparing effects of various azole antifungal agents on hepatic CYP3A4 activity.

Abstract: Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of DILI presentations by a single drug suggests that both drugs and host factors may contribute to the phenotype. However, factors determining the DILI types have not been yet elucidated. Identifying such factors may help to accurately predict the injury types based on drugs and host information and assist the clinical diagnosis of DILI. Using prospective DILI registry datasets, we sought to explore and validate the associations of biochemical injury types at the time of DILI recognition with comprehensive information on drug properties and host factors. Random forest models identified a set of drug properties and host factors that differentiate hepatocellular from cholestatic damage with reasonable accuracy (69-84%). A simplified logistic regression model developed for practical use, consisting of patient's age, drug's lipoaffinity, and hybridization ratio, achieved a fair prediction (68-74%), but suggested potential clinical usability, computing the likelihood of liver injury type based on two properties of drugs taken by a patient and patient's age. In summary, considering both drug and host factors in evaluating DILI risk and phenotypes open an avenue for future DILI research and aid in the refinement of causality assessment.

Abstract: The outbreak of the novel coronavirus severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) respiratory disease, led to a global pandemic with high morbidity and mortality. Despite frenzied efforts in therapeutic development, there are currently no effective drugs for treatment, nor are there vaccines for its prevention. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, is one of the most practical treatment options against the outbreak. In this study, we present a novel strategy for in silico molecular modeling screening for potential drugs that may interact with multiple main proteins of SARS-CoV-2. Targeting multiple viral proteins is a novel drug discovery concept in that it enables the potential drugs to act on different stages of the virus' life cycle, thereby potentially maximizing the drug potency. We screened 2631 US Food and Drug Administration (FDA)-approved small molecules against 4 key proteins of SARS-CoV-2 that are known as attractive targets for antiviral drug development. In total, we identified 29 drugs that could actively interact with 2 or more target proteins, with 5 drugs (avapritinib, bictegravir, ziprasidone, capmatinib, and pexidartinib) being common candidates for all 4 key host proteins and 3 of them possessing the desirable molecular properties. By overlaying docked positions of drug candidates onto individual host proteins, it has been further confirmed that the binding site conformations are conserved. The drugs identified in our screening provide potential guidance for experimental confirmation, such as in vitro molecular assays and in vivo animal testing, as well as incorporation into ongoing clinical studies.

Abstract: BACKGROUND: The USA is in the midst of an opioid overdose epidemic. To address the epidemic, we conducted a large-scale population study on opioid overdose. OBJECTIVES: The primary objective of this study was to evaluate the temporal trends and risk factors of inpatient opioid overdose. Based on its patterns, the secondary objective was to examine the innate properties of opioid analgesics underlying reduced overdose effects. METHODS: A retrospective cross-sectional study was conducted based on a large-scale inpatient electronic health records database, Cerner Health Facts((R)), with (1) inclusion criteria for participants as patients admitted between 1 January, 2009 and 31 December, 2017 and (2) measurements as opioid overdose prevalence by year, demographics, and prescription opioid exposures. RESULTS: A total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts((R)). Among them, 30.2% patients were aged 65+ years, 57.0% female, 70.1% Caucasian, 42.3% single, 32.0% from the South, and 80.8% in an urban area. From 2009 to 2017, annual opioid overdose prevalence per 1000 patients significantly increased from 3.7 to 11.9 with an adjusted odds ratio (aOR): 1.16, 95% confidence interval (CI) 1.15-1.16. Compared to the major demographic counterparts, being in (1) age group: 41-50 years (overall aOR 1.36, 95% CI 1.31-1.40) or 51-64 years (overall aOR 1.35, 95% CI 1.32-1.39), (2) marital status: divorced (overall aOR 1.19, 95% CI 1.15-1.23), and (3) census region: West (overall aOR 1.32, 95% CI 1.28-1.36) were significantly associated with a higher odds of opioid overdose. Prescription opioid exposures were also associated with an increased odds of opioid overdose, such as meperidine (overall aOR 1.09, 95% CI 1.06-1.13) and tramadol (overall aOR 2.20, 95% CI 2.14-2.27). Examination on the relationships between opioid analgesic properties and their association strengths, aORs, and opioid overdose showed that lower aOR values were significantly associated with (1) high molecular weight, (2) non-interaction with multi-drug resistance protein 1 or interaction with cytochrome P450 3A4, and (3) non-interaction with the delta opioid receptor or kappa opioid receptor. CONCLUSIONS: The significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 suggested an ongoing need for efforts to combat the opioid overdose epidemic in the USA. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Moreover, there are physicochemical, pharmacokinetic, and pharmacodynamic properties underlying reduced overdose effects, which can be utilized to develop better opioids.

Abstract: Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.

Abstract: Here we report the synthesis and application of trifluoromethyl thianthrenium triflate (TT-CF3(+)OTf(-)) as a novel trifluoromethylating reagent, which is conveniently accessible in a single step from thianthrene and triflic anhydride. We demonstrate the use of TT-CF3(+)OTf(-) in electrophilic, radical, and nucleophilic trifluoromethylation reactions.

Abstract: BACKGROUND: Checkpoint inhibitors and currently approved cellular products for metastatic castration-resistant prostate cancer have not resulted in revolutionary changes in outcomes compared to other solid tumors. Much of this lack of progress is attributed to the unique tumor microenvironment of prostate cancer that is often immunologically cold and immunosuppressive. These unique conditions emphasize the need for novel therapeutic options. In this review, we will discuss progress made in design of T- and NK cell immune engagers in addition to chimeric antigen receptor products specifically designed for prostate cancer that are currently under investigation in clinical trials. METHODS: We searched peer-reviewed literature on the PubMed and the ClinicalTrials.gov databases for active clinical trials using the terms "bispecific T-cell engager," "bispecific killer engager," "trispecific killer engager," "chimeric antigen receptor," "metastatic castration-resistant prostate cancer," and "neuroendocrine prostate cancer." RESULTS: Ten bispecific T-cell engager studies and nine chimeric antigen receptor-based products were found. Published data were compiled and presented based on therapeutic class. CONCLUSIONS: Multiple immune engagers and cell therapies are in the development pipeline and demonstrate promise to address barriers to better outcomes for metastatic castration-resistant prostate cancer patients.

Abstract: Despite improvements in understanding the pathogenic mechanisms of primary glomerular diseases, therapy still remains nonspecific. We sought to identify novel therapies targeting kidney-intrinsic injury of distinct primary glomerulonephritides through computational systems biology approaches. We defined the unique transcriptional landscape within kidneys from patients with focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD), immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN) and thin basement membrane nephropathy (TBMN). Differentially expressed genes were functionally annotated with enrichment analysis, and distinct biological processes and pathways implicated in each primary glomerular disease were uncovered. Finally, we identified novel drugs and small-molecule compounds that may reverse each glomerulonephritis phenotype, suggesting they should be further tested as precise therapy in primary glomerular diseases.

Abstract: The indole alkaloid yohimbine is an alpha-2 receptor antagonist used for its sympathomimetic effects. Several cases of yohimbine intoxication have been reported and the most recent one involved four individuals taking a yohimbine-containing drug powder. All individuals developed severe intoxication symptoms and were admitted to the hospital. Even though all individuals were assumed to have taken the same dose of the drug powder, toxicology analyses revealed yohimbine blood concentrations of 249-5631 ng/mL, amounting to a 22-fold difference. The reason for this high variability remained to be elucidated. We used recently reported knowledge on the metabolism of yohimbine together with state-of-the art nonlinear mixed-effects modelling and simulation and show that a patient's cytochrome P450 2D6 (CYP2D6) phenotype can explain the large differences observed in the measured concentration after intake of the same yohimbine dose. Our findings can be used both for the identification of safe doses in therapeutic use of yohimbine and for an explanation of individual cases of overdosing.